Comparability of Objective Structured Clinical Examinations (OSCEs) and Written Tests for Assessing Medical School Students’ Competencies: A Scoping Review
Bibliographic record
Abstract
Objective Structured Clinical Examinations (OSCEs) and written tests are commonly used to assess health professional students, but it remains unclear whether the additional human resources and expenses required for OSCEs, both in-person and online, are worthwhile for assessing competencies. This scoping review summarized literature identified by searching MEDLINE and EMBASE comparing 1) OSCEs and written tests and 2) in-person and online OSCEs, for assessing health professional trainees’ competencies. For Q1, 21 studies satisfied inclusion criteria. The most examined health profession was medical trainees (19, 90.5%), the comparison was most frequently OSCEs versus multiple-choice questions (MCQs) (18, 85.7%), and 18 (87.5%) examined the same competency domain. Most (77.5%) total score correlation coefficients between testing methods were weak ( r < 0.40). For Q2, 13 articles were included. In-person and online OSCEs were most used for medical trainees (9, 69.2%), checklists were the most prevalent evaluation scheme (7, 63.6%), and 14/17 overall score comparisons were not statistically significantly different. Generally low correlations exist between MCQ and OSCE scores, providing insufficient evidence as to whether OSCEs provide sufficient value to be worth their additional cost. Online OSCEs may be a viable alternative to in-person OSCEs for certain competencies where technical challenges can be met.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.078 | 0.301 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.043 | 0.027 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".